Dynamic magnetic resonance imaging (MRI) involves creating a sequence of magnetic resonance (MR) images to monitor temporal changes in an object of interest (e.g., tissue structure). Dynamic MRI apparatus seek to acquire images as fast as possible while maintaining a sufficient signal to noise ratio (SNR) to investigate the object being imaged. Partial parallel acquisition (PPA) strategies that facilitate accelerating image acquisition are therefore employed in dynamic MRI.
For example, there are dynamic parallel MRI (DpMRI) approaches based on both TSENSE (time adaptive sensitivity encoding) and TGRAPPA (temporal generalized auto-calibrating partially parallel acquisitions). Both TSENSE and TGRAPPA are based on a time-interleaved phase-encoding (PE) scheme. Conventionally, at high acceleration factors, both TSENSE and TGRAPPA have experienced noise enhancement. This noise enhancement may lead to an unacceptable signal to noise ratio (SNR) in reconstructed images. In both TSENSE and TGRAPPA, a fully Fourier-encoded composite data set may be assembled. This composite data set may be used to calculate parameters (e.g., weights, coil sensitivity profiles) used in parallel image reconstruction. These parameters can then be used to reconstruct individual under-sampled time frames.